Prompt with me: Building a ReAct self-coding AI agent from scratch - no LangChain or code!

Robert Chandler
1 Nov 202312:45

TLDRIn this tutorial, the presenter guides viewers through building a simple ReAct self-coding AI agent in Wordware, designed to solve math problems. The agent operates on a thought-action-input-observation loop, utilizing tools like a JavaScript code interpreter and Google search. The process involves defining actions, inputs, and observations within a loop, iterating up to 10 times to refine the solution. The example demonstrates solving a math power operation, highlighting the agent's ability to autonomously decide and execute tasks.

Takeaways

  • 😀 The tutorial aims to build a simple self-coding AI agent in 10 minutes without using LangChain or code.
  • 🤖 An agent is defined as an AI-powered entity, typically driven by language models, capable of autonomous decision-making.
  • 🔄 The agent operates on a 'react' model, which includes a thought-action-input-observation loop to solve tasks.
  • 🧠 The agent is designed to solve math problems using specified tools, such as a JavaScript code interpreter and Google search.
  • 🛠️ The 'run code' tool executes JavaScript code and returns the output, while the 'Google search' tool performs internet searches based on input queries.
  • 📝 The agent follows a structured format for problem-solving, which includes defining a question, thought, action, input, observation, and final answer.
  • 🔢 The example given is to calculate (99,999) to the power of 5 using the 'run code' tool.
  • 🔍 The agent includes a loop mechanism, referred to as the 'react loop,' with a maximum of 10 iterations to find a solution.
  • 🚫 The agent must ensure that actions are valid, distinguishing between 'run code' and 'Google search' and handling errors appropriately.
  • 🔚 The final output is a concise final answer, which the agent determines after completing the thought-action cycle.
  • 🔄 The process involves debugging and refining the agent's actions, such as fixing syntax errors in code and adjusting input formats.

Q & A

  • What is the main purpose of the 'React agent' discussed in the transcript?

    -The main purpose of the 'React agent' is to autonomously decide what to do and execute actions to solve tasks, particularly math problems in this context, by utilizing tools and language models.

  • What are the core components of a React agent's thought-action-input-observation loop?

    -The core components of a React agent's loop are thinking about what to do next, deciding on an action from a set of allowed actions, executing the action, and observing the result of the action.

  • How does the agent utilize tools to solve math problems?

    -The agent uses tools such as a JavaScript code interpreter to execute mathematical operations and a Google search function to find information, which aids in solving math problems.

  • What is the structure that the agent should follow according to the transcript?

    -The agent should follow a structure that includes a question, a thought about what to do, an action to take (either 'Google search' or 'run code'), the input for the action, and the observation which is the result of the action.

  • What is the maximum number of iterations set for the React Loop in the script?

    -The maximum number of iterations set for the React Loop is 10.

  • How does the agent decide whether to run code or perform a Google search?

    -The agent decides based on the thought process and the nature of the task. If it requires executing a mathematical operation, it will run code; if it requires information gathering, it will perform a Google search.

  • What is the role of the observation logs in the agent's process?

    -Observation logs record the results of the actions taken by the agent, which helps in understanding the outcome of each step and informs the subsequent decisions.

  • How does the agent ensure that the input for the 'run code' action is valid JavaScript code?

    -The agent checks if the input is a valid JavaScript code by using a prompt that specifically handles code execution and ensures that the input is formatted correctly.

  • What happens if the agent chooses an invalid action?

    -If the agent chooses an invalid action, it will be informed that the action is not valid and should only be 'Google search' or 'run code'.

  • What is the final output of the agent after going through the thought-action-input-observation loop?

    -The final output is the solution to the math problem, which the agent arrives at after iterating through the loop and making decisions based on observations.

  • How does the agent handle errors or unexpected results during the process?

    -The agent handles errors by iterating through the loop, making adjustments based on observations, and ensuring that the final answer is correct and valid.

Outlines

00:00

🤖 Building a Simple AI Agent for Math Problems

The script introduces the process of creating a simple AI agent within Wordware, aimed at solving mathematical problems. The agent is described as a 'react agent' that operates on a thought, action, and observation loop. It uses language models for decision-making and can utilize tools like a JavaScript code interpreter and Google search. The agent is designed to think about its next move, choose an action from a set of allowed actions, execute it, and observe the result. The script outlines the structure for the agent's operation and emphasizes the importance of using the right tools and format for problem-solving.

05:02

🔁 The React Loop for Iterative Problem Solving

This paragraph delves into the specifics of the 'react loop', which is a core component of the AI agent's operation. The loop is set to iterate up to 10 times, allowing the agent to process user input, think, decide on an action, and execute it. Actions can include running code or performing a Google search. The paragraph emphasizes the importance of input validation and the need for the agent to understand and differentiate between running code and performing a search. It also discusses the need for the agent to output clear logs and to stop the action loop when it starts outputting observations.

10:02

🛠️ Refining the Agent's Code Execution and Search Process

The final paragraph discusses the refinement of the agent's code execution and search process. It highlights the need to ensure that the agent correctly formats its input, particularly when dealing with mathematical operations. The paragraph also addresses a specific issue encountered during testing, where the agent incorrectly added asterisks, leading to an illegal return statement. The solution involves adjusting the input format to prevent such errors. The agent's ability to loop and decide when to stop is also emphasized, with the final output being the agent's final answer after considering all iterations.

Mindmap

Keywords

💡ReAct self-coding AI agent

A ReAct self-coding AI agent, as described in the video, is an autonomous system that can make decisions and perform actions based on its programming. It is powered by language models and is designed to solve complex tasks that cannot be directly addressed through simple chat interactions. In the context of the video, the agent is built to solve mathematical problems using tools like a JavaScript code interpreter and Google search.

💡LangChain

LangChain is mentioned as something the video will not use in building the AI agent. It is likely a reference to a specific framework or toolchain for developing language-based AI applications. The script suggests that the agent will be built 'from scratch,' indicating that the process will not rely on pre-existing solutions like LangChain.

💡Wordware

Wordware is the environment or platform where the agent is being built, as mentioned in the script. It seems to be a tool or software that facilitates the creation of AI agents, allowing for the scripting and implementation of their decision-making processes.

💡Agent

In the video, an 'agent' is defined as an AI-powered entity capable of autonomous decision-making. It is generally powered by language models and can utilize tools to perform tasks. The agent in the script is designed to solve mathematical problems by deciding on actions and utilizing specified tools.

💡Thought, Action, Input, and Observation Loop

This concept refers to the cyclical process that the AI agent follows: it thinks about what to do next, decides on an action, takes that action with a specified input, and then observes the result. This loop is central to the agent's operation, as it allows for continuous learning and adaptation based on the outcomes of its actions.

💡Math problem-solving agent

The specific type of agent being built in the video is a 'math problem-solving agent.' This agent is designed to answer mathematical questions using tools at its disposal. It demonstrates the application of AI in specialized domains, such as mathematics, where complex problems require precise solutions.

💡JavaScript code interpreter

One of the tools the agent can use is a 'JavaScript code interpreter,' which allows it to execute JavaScript code. This tool is essential for solving mathematical problems that can be expressed as code, providing a means for the agent to perform calculations and return results.

💡Google search

The agent is also capable of performing 'Google searches' to find information. This tool expands the agent's knowledge base and allows it to seek answers to questions that may not be directly solvable through code execution alone.

💡Rules

The script mentions following certain 'rules' for the agent's operation. These rules likely pertain to the structure and logic of the agent's decision-making process, ensuring that it operates within predefined parameters and follows a consistent methodology.

💡React Loop

The 'React Loop' is a specific part of the agent's operation where it repeatedly goes through the thought, action, input, and observation cycle. The loop is designed to run a maximum of 10 times, as mentioned in the script, to prevent infinite loops and ensure the agent reaches a conclusion.

💡Final answer

After going through the React Loop, the agent is expected to provide a 'final answer' to the mathematical problem. This represents the culmination of the agent's decision-making process and the output of its actions, signifying the resolution of the problem at hand.

Highlights

Introduction to building a simple AI agent in Wordware without using LangChain or code.

Definition of an agent as an AI-powered, autonomous decision-making entity.

Explanation of a React agent with a thought, action, input, and observation loop.

The agent's purpose to solve math problems using specified tools.

Description of the first tool: a JavaScript code interpreter for executing math operations.

Capability of the agent to search Google for information.

Introduction of the 'rules' structure for the agent's decision-making process.

Format for the agent's thought process and actions based on the paper's guidelines.

The React Loop with a maximum of 10 iterations for problem-solving.

Process of deciding on an action: Google search or running code.

Implementation of a prompt to run JavaScript code.

Utilization of another prompt for performing Google searches.

Handling invalid actions with an error statement.

Final output of the agent's answer after the thought and action process.

Example of the agent solving a math problem with JavaScript code.

Identification of issues with input formatting and the need for adjustments.

Iteration and refinement of the agent's process to improve accuracy.

Finalization of the agent's response after 10 loops with the correct answer.